1
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Wang BX, Leshchiner D, Luo L, Tuncel M, Hokamp K, Hinton JCD, Monack DM. High-throughput fitness experiments reveal specific vulnerabilities of human-adapted Salmonella during stress and infection. Nat Genet 2024; 56:1288-1299. [PMID: 38831009 PMCID: PMC11176087 DOI: 10.1038/s41588-024-01779-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 04/25/2024] [Indexed: 06/05/2024]
Abstract
Salmonella enterica is comprised of genetically distinct 'serovars' that together provide an intriguing model for exploring the genetic basis of pathogen evolution. Although the genomes of numerous Salmonella isolates with broad variations in host range and human disease manifestations have been sequenced, the functional links between genetic and phenotypic differences among these serovars remain poorly understood. Here, we conduct high-throughput functional genomics on both generalist (Typhimurium) and human-restricted (Typhi and Paratyphi A) Salmonella at unprecedented scale in the study of this enteric pathogen. Using a comprehensive systems biology approach, we identify gene networks with serovar-specific fitness effects across 25 host-associated stresses encountered at key stages of human infection. By experimentally perturbing these networks, we characterize previously undescribed pseudogenes in human-adapted Salmonella. Overall, this work highlights specific vulnerabilities encoded within human-restricted Salmonella that are linked to the degradation of their genomes, shedding light into the evolution of this enteric pathogen.
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Affiliation(s)
- Benjamin X Wang
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Lijuan Luo
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Miles Tuncel
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | - Karsten Hokamp
- Department of Genetics, School of Genetics and Microbiology, Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland
| | - Jay C D Hinton
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Denise M Monack
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA.
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2
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Periyasamy S, Youssef P, John S, Thara R, Mowry BJ. Genetic interactions of schizophrenia using gene-based statistical epistasis exclusively identify nervous system-related pathways and key hub genes. Front Genet 2024; 14:1301150. [PMID: 38259618 PMCID: PMC10800577 DOI: 10.3389/fgene.2023.1301150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 12/12/2023] [Indexed: 01/24/2024] Open
Abstract
Background: The relationship between genotype and phenotype is governed by numerous genetic interactions (GIs), and the mapping of GI networks is of interest for two main reasons: 1) By modelling biological robustness, GIs provide a powerful opportunity to infer compensatory biological mechanisms via the identification of functional relationships between genes, which is of interest for biological discovery and translational research. Biological systems have evolved to compensate for genetic (i.e., variations and mutations) and environmental (i.e., drug efficacy) perturbations by exploiting compensatory relationships between genes, pathways and biological processes; 2) GI facilitates the identification of the direction (alleviating or aggravating interactions) and magnitude of epistatic interactions that influence the phenotypic outcome. The generation of GIs for human diseases is impossible using experimental biology approaches such as systematic deletion analysis. Moreover, the generation of disease-specific GIs has never been undertaken in humans. Methods: We used our Indian schizophrenia case-control (case-816, controls-900) genetic dataset to implement the workflow. Standard GWAS sample quality control procedure was followed. We used the imputed genetic data to increase the SNP coverage to analyse epistatic interactions across the genome comprehensively. Using the odds ratio (OR), we identified the GIs that increase or decrease the risk of a disease phenotype. The SNP-based epistatic results were transformed into gene-based epistatic results. Results: We have developed a novel approach by conducting gene-based statistical epistatic analysis using an Indian schizophrenia case-control genetic dataset and transforming these results to infer GIs that increase the risk of schizophrenia. There were ∼9.5 million GIs with a p-value ≤ 1 × 10-5. Approximately 4.8 million GIs showed an increased risk (OR > 1.0), while ∼4.75 million GIs had a decreased risk (OR <1.0) for schizophrenia. Conclusion: Unlike model organisms, this approach is specifically viable in humans due to the availability of abundant disease-specific genome-wide genotype datasets. The study exclusively identified brain/nervous system-related processes, affirming the findings. This computational approach fills a critical gap by generating practically non-existent heritable disease-specific human GIs from human genetic data. These novel datasets can train innovative deep-learning models, potentially surpassing the limitations of conventional GWAS.
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Affiliation(s)
- Sathish Periyasamy
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Wacol, QLD, Australia
| | - Pierre Youssef
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Sujit John
- Schizophrenia Research Foundation, Chennai, Tamil Nadu, India
| | | | - Bryan J. Mowry
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Wacol, QLD, Australia
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3
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Tomaz KCP, Tavella TA, Borba JVB, Salazar-Alvarez LC, Levandoski JE, Mottin M, Sousa BKP, Moreira-Filho JT, Almeida VM, Clementino LC, Bourgard C, Massirer KB, Couñago RM, Andrade CH, Sunnerhagen P, Bilsland E, Cassiano GC, Costa FTM. Identification of potential inhibitors of casein kinase 2 alpha of Plasmodium falciparum with potent in vitro activity. Antimicrob Agents Chemother 2023; 67:e0058923. [PMID: 37819090 PMCID: PMC10649021 DOI: 10.1128/aac.00589-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 08/11/2023] [Indexed: 10/13/2023] Open
Abstract
Drug resistance to commercially available antimalarials is a major obstacle in malaria control and elimination, creating the need to find new antiparasitic compounds with novel mechanisms of action. The success of kinase inhibitors for oncological treatments has paved the way for the exploitation of protein kinases as drug targets in various diseases, including malaria. Casein kinases are ubiquitous serine/threonine kinases involved in a wide range of cellular processes such as mitotic checkpoint signaling, DNA damage response, and circadian rhythm. In Plasmodium, it is suggested that these protein kinases are essential for both asexual and sexual blood-stage parasites, reinforcing their potential as targets for multi-stage antimalarials. To identify new putative PfCK2α inhibitors, we utilized an in silico chemogenomic strategy involving virtual screening with docking simulations and quantitative structure-activity relationship predictions. Our investigation resulted in the discovery of a new quinazoline molecule (542), which exhibited potent activity against asexual blood stages and a high selectivity index (>100). Subsequently, we conducted chemical-genetic interaction analysis on yeasts with mutations in casein kinases. Our chemical-genetic interaction results are consistent with the hypothesis that 542 inhibits yeast Cka1, which has a hinge region with high similarity to PfCK2α. This finding is in agreement with our in silico results suggesting that 542 inhibits PfCK2α via hinge region interaction.
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Affiliation(s)
- Kaira C. P. Tomaz
- Laboratory of Tropical Diseases (LDT), Institute of Biology, University of Campinas, Campinas, Brazil
| | - Tatyana A. Tavella
- Laboratory of Tropical Diseases (LDT), Institute of Biology, University of Campinas, Campinas, Brazil
| | - Joyce V. B. Borba
- Laboratory of Tropical Diseases (LDT), Institute of Biology, University of Campinas, Campinas, Brazil
- Laboratory of Molecular Modeling and Drug Design (LabMol), Faculty of Pharmacy, Universidade Federal de Goiás (UFG), Goiânia, Brazil
| | - Luis C. Salazar-Alvarez
- Laboratory of Tropical Diseases (LDT), Institute of Biology, University of Campinas, Campinas, Brazil
| | - João E. Levandoski
- Department of Materials and Bioprocesses Engineering, School of Chemical Engineering, University of Campinas, Campinas, Brazil
| | - Melina Mottin
- Laboratory of Molecular Modeling and Drug Design (LabMol), Faculty of Pharmacy, Universidade Federal de Goiás (UFG), Goiânia, Brazil
| | - Bruna K. P. Sousa
- Laboratory of Molecular Modeling and Drug Design (LabMol), Faculty of Pharmacy, Universidade Federal de Goiás (UFG), Goiânia, Brazil
| | - José T. Moreira-Filho
- Laboratory of Molecular Modeling and Drug Design (LabMol), Faculty of Pharmacy, Universidade Federal de Goiás (UFG), Goiânia, Brazil
| | - Vitor M. Almeida
- Centro de Química Medicinal (CQMED), Centro de Biologia Molecular e Engenharia Genética(CBMEG), Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
| | - Leandro C. Clementino
- Laboratory of Tropical Diseases (LDT), Institute of Biology, University of Campinas, Campinas, Brazil
| | - Catarina Bourgard
- Laboratory of Tropical Diseases (LDT), Institute of Biology, University of Campinas, Campinas, Brazil
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
| | - Katlin B. Massirer
- Centro de Química Medicinal (CQMED), Centro de Biologia Molecular e Engenharia Genética(CBMEG), Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
| | - Rafael M. Couñago
- Centro de Química Medicinal (CQMED), Centro de Biologia Molecular e Engenharia Genética(CBMEG), Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
- Structural Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Carolina H. Andrade
- Laboratory of Molecular Modeling and Drug Design (LabMol), Faculty of Pharmacy, Universidade Federal de Goiás (UFG), Goiânia, Brazil
- Center for Research and Advancement of Fragments and Molecular Targets (CRAFT), University of São Paulo, São Paulo, Brazil
- Center for Excellence in Artificial Intelligence (CEIA), Institute of Informatics, Universidade Federal de Goiás, Goiânia, Brazil
| | - Per Sunnerhagen
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
| | - Elizabeth Bilsland
- Department of Structural and Functional Biology, Synthetic Biology Laboratory, Institute of Biology, University of Campinas, Campinas, Brazil
| | - Gustavo C. Cassiano
- Laboratory of Tropical Diseases (LDT), Institute of Biology, University of Campinas, Campinas, Brazil
- Global Health and Tropical Medicine (GHTM), Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisbon, Portugal
| | - Fabio T. M. Costa
- Laboratory of Tropical Diseases (LDT), Institute of Biology, University of Campinas, Campinas, Brazil
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Chen X, Li X, Ji B, Wang Y, Ishchuk OP, Vorontsov E, Petranovic D, Siewers V, Engqvist MK. Suppressors of amyloid-β toxicity improve recombinant protein production in yeast by reducing oxidative stress and tuning cellular metabolism. Metab Eng 2022; 72:311-324. [DOI: 10.1016/j.ymben.2022.04.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 04/22/2022] [Accepted: 04/25/2022] [Indexed: 12/24/2022]
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5
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A proximity-dependent biotinylation map of a human cell. Nature 2021; 595:120-124. [PMID: 34079125 DOI: 10.1038/s41586-021-03592-2] [Citation(s) in RCA: 203] [Impact Index Per Article: 67.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 04/29/2021] [Indexed: 02/05/2023]
Abstract
Compartmentalization is a defining characteristic of eukaryotic cells, and partitions distinct biochemical processes into discrete subcellular locations. Microscopy1 and biochemical fractionation coupled with mass spectrometry2-4 have defined the proteomes of a variety of different organelles, but many intracellular compartments have remained refractory to such approaches. Proximity-dependent biotinylation techniques such as BioID provide an alternative approach to define the composition of cellular compartments in living cells5-7. Here we present a BioID-based map of a human cell on the basis of 192 subcellular markers, and define the intracellular locations of 4,145 unique proteins in HEK293 cells. Our localization predictions exceed the specificity of previous approaches, and enabled the discovery of proteins at the interface between the mitochondrial outer membrane and the endoplasmic reticulum that are crucial for mitochondrial homeostasis. On the basis of this dataset, we created humancellmap.org as a community resource that provides online tools for localization analysis of user BioID data, and demonstrate how this resource can be used to understand BioID results better.
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6
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τ-SGA: synthetic genetic array analysis for systematically screening and quantifying trigenic interactions in yeast. Nat Protoc 2021; 16:1219-1250. [PMID: 33462440 PMCID: PMC9127509 DOI: 10.1038/s41596-020-00456-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 10/28/2020] [Indexed: 01/29/2023]
Abstract
Systematic complex genetic interaction studies have provided insight into high-order functional redundancies and genetic network wiring of the cell. Here, we describe a method for screening and quantifying trigenic interactions from ordered arrays of yeast strains grown on agar plates as individual colonies. The protocol instructs users on the trigenic synthetic genetic array analysis technique, τ-SGA, for high-throughput screens. The steps describe construction of the double-mutant query strains and the corresponding single-mutant control query strains, which are screened in parallel in two replicates. The screening experimental set-up consists of sequential replica-pinning steps that enable automated mating, meiotic recombination and successive haploid selection steps for the generation of triple mutants, which are scored for colony size as a proxy for fitness, which enables the calculation of trigenic interactions. The procedure described here was used to conduct 422 trigenic interaction screens, which generated ~460,000 yeast triple mutants for trigenic interaction analysis. Users should be familiar with robotic equipment required for high-throughput genetic interaction screens and be proficient at the command line to execute the scoring pipeline. Large-scale screen computational analysis is achieved by using MATLAB pipelines that score raw colony size data to produce τ-SGA interaction scores. Additional recommendations are included for optimizing experimental design and analysis of smaller-scale trigenic interaction screens by using a web-based analysis system, SGAtools. This protocol provides a resource for those who would like to gain a deeper, more practical understanding of trigenic interaction screening and quantification methodology.
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Kuzmin E, Andrews BJ, Boone C. Trigenic Synthetic Genetic Array (τ-SGA) Technique for Complex Interaction Analysis. Methods Mol Biol 2021; 2212:377-400. [PMID: 33733368 DOI: 10.1007/978-1-0716-0947-7_23] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Complex genetic interactions occur when mutant alleles of multiple genes combine to elicit an unexpected phenotype, which could not be predicted given the expectation based on the combination of phenotypes associated with individual mutant alleles. Trigenic Synthetic Genetic Array (τ-SGA) methodology was developed for the systematic analysis of complex interactions involving combinations of three gene perturbations. With a series of replica pinning steps of the τ-SGA procedure, haploid triple mutants are constructed through automated mating and meiotic recombination. For example, a double-mutant query strain carrying two mutant alleles of interest, such as a deletion allele of a nonessential gene and a conditional temperature-sensitive allele of an essential gene, is crossed to an input array of yeast mutants, such as the diagnostic array set of ~1200 mutants, to generate an output array of triple mutants. The colony-size measurements of the resulting triple mutants are used to estimate cellular fitness and quantify trigenic interactions by incorporating corresponding single- and double-mutant fitness estimates. Trigenic interaction networks can be further analyzed for functional modules using various clustering and enrichment analysis tools. Complex genetic interactions are rich in functional information and provide insight into the genotype-to-phenotype relationship, genome size, and speciation.
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Affiliation(s)
- Elena Kuzmin
- Goodman Cancer Research Centre, McGill University, Montreal, QC, Canada
| | - Brenda J Andrews
- The Donnelly Centre, Toronto, ON, Canada. .,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
| | - Charles Boone
- The Donnelly Centre, Toronto, ON, Canada. .,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
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8
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Chen X, Ji B, Hao X, Li X, Eisele F, Nyström T, Petranovic D. FMN reduces Amyloid-β toxicity in yeast by regulating redox status and cellular metabolism. Nat Commun 2020; 11:867. [PMID: 32054832 PMCID: PMC7018843 DOI: 10.1038/s41467-020-14525-4] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 01/07/2020] [Indexed: 01/09/2023] Open
Abstract
Alzheimer's disease (AD) is defined by progressive neurodegeneration, with oligomerization and aggregation of amyloid-β peptides (Aβ) playing a pivotal role in its pathogenesis. In recent years, the yeast Saccharomyces cerevisiae has been successfully used to clarify the roles of different human proteins involved in neurodegeneration. Here, we report a genome-wide synthetic genetic interaction array to identify toxicity modifiers of Aβ42, using yeast as the model organism. We find that FMN1, the gene encoding riboflavin kinase, and its metabolic product flavin mononucleotide (FMN) reduce Aβ42 toxicity. Classic experimental analyses combined with RNAseq show the effects of FMN supplementation to include reducing misfolded protein load, altering cellular metabolism, increasing NADH/(NADH + NAD+) and NADPH/(NADPH + NADP+) ratios and increasing resistance to oxidative stress. Additionally, FMN supplementation modifies Htt103QP toxicity and α-synuclein toxicity in the humanized yeast. Our findings offer insights for reducing cytotoxicity of Aβ42, and potentially other misfolded proteins, via FMN-dependent cellular pathways.
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Affiliation(s)
- Xin Chen
- Division of Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, SE41296, Gothenburg, Sweden
| | - Boyang Ji
- Division of Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, SE41296, Gothenburg, Sweden
| | - Xinxin Hao
- Institute for Biomedicine, Sahlgrenska Academy, Centre for Ageing and Health-AgeCap, University of Gothenburg, SE40530, Gothenburg, Sweden
| | - Xiaowei Li
- Division of Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296, Gothenburg, Sweden
| | - Frederik Eisele
- Institute for Biomedicine, Sahlgrenska Academy, Centre for Ageing and Health-AgeCap, University of Gothenburg, SE40530, Gothenburg, Sweden
| | - Thomas Nyström
- Institute for Biomedicine, Sahlgrenska Academy, Centre for Ageing and Health-AgeCap, University of Gothenburg, SE40530, Gothenburg, Sweden
| | - Dina Petranovic
- Division of Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296, Gothenburg, Sweden.
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, SE41296, Gothenburg, Sweden.
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9
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Law HCH, Lagundžin D, Clement EJ, Qiao F, Wagner ZS, Krieger KL, Costanzo-Garvey D, Caffrey TC, Grem JL, DiMaio DJ, Grandgenett PM, Cook LM, Fisher KW, Yu F, Hollingsworth MA, Woods NT. The Proteomic Landscape of Pancreatic Ductal Adenocarcinoma Liver Metastases Identifies Molecular Subtypes and Associations with Clinical Response. Clin Cancer Res 2019; 26:1065-1076. [PMID: 31848187 DOI: 10.1158/1078-0432.ccr-19-1496] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 10/19/2019] [Accepted: 12/11/2019] [Indexed: 12/16/2022]
Abstract
PURPOSE Pancreatic ductal adenocarcinoma (PDAC) is a highly metastatic disease that can be separated into distinct subtypes based on molecular signatures. Identifying PDAC subtype-specific therapeutic vulnerabilities is necessary to develop precision medicine approaches to treat PDAC. EXPERIMENTAL DESIGN A total of 56 PDAC liver metastases were obtained from the UNMC Rapid Autopsy Program and analyzed with quantitative proteomics. PDAC subtypes were identified by principal component analysis based on protein expression profiling. Proteomic subtypes were further characterized by the associated clinical information, including but not limited to survival analysis, drug treatment response, and smoking and drinking status. RESULTS Over 3,960 proteins were identified and used to delineate four distinct PDAC microenvironment subtypes: (i) metabolic; (ii) progenitor-like; (iii) proliferative; and (iv) inflammatory. PDAC risk factors of alcohol and tobacco consumption correlate with subtype classifications. Enhanced survival is observed in FOLFIRINOX treated metabolic and progenitor-like subtypes compared with the proliferative and inflammatory subtypes. In addition, TYMP, PDCD6IP, ERAP1, and STMN showed significant association with patient survival in a subtype-specific manner. Gemcitabine-induced alterations in the proteome identify proteins, such as serine hydroxymethyltransferase 1, associated with drug resistance. CONCLUSIONS These data demonstrate that proteomic analysis of clinical PDAC liver metastases can identify molecular signatures unique to disease subtypes and point to opportunities for therapeutic development to improve the treatment of PDAC.
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Affiliation(s)
- Henry C-H Law
- Eppley Institute for Research in Cancer, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, Nebraska
| | - Dragana Lagundžin
- Eppley Institute for Research in Cancer, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, Nebraska
| | - Emalie J Clement
- Eppley Institute for Research in Cancer, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, Nebraska
| | - Fangfang Qiao
- Eppley Institute for Research in Cancer, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, Nebraska
| | - Zachary S Wagner
- Eppley Institute for Research in Cancer, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, Nebraska
| | - Kimiko L Krieger
- Eppley Institute for Research in Cancer, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, Nebraska
| | - Diane Costanzo-Garvey
- Department of Pathology and Microbiology, College of Medicine, University of Nebraska Medical Center, Omaha Nebraska
| | - Thomas C Caffrey
- Eppley Institute for Research in Cancer, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, Nebraska
| | - Jean L Grem
- Internal Medicine, Division of Hematology Oncology, University of Nebraska Medical Center, Omaha Nebraska
| | - Dominick J DiMaio
- Department of Pathology and Microbiology, College of Medicine, University of Nebraska Medical Center, Omaha Nebraska
| | - Paul M Grandgenett
- Eppley Institute for Research in Cancer, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, Nebraska
| | - Leah M Cook
- Department of Pathology and Microbiology, College of Medicine, University of Nebraska Medical Center, Omaha Nebraska
| | - Kurt W Fisher
- Department of Pathology and Microbiology, College of Medicine, University of Nebraska Medical Center, Omaha Nebraska
| | - Fang Yu
- Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha Nebraska
| | - Michael A Hollingsworth
- Eppley Institute for Research in Cancer, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, Nebraska
| | - Nicholas T Woods
- Eppley Institute for Research in Cancer, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, Nebraska.
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10
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Nelson W, Zitnik M, Wang B, Leskovec J, Goldenberg A, Sharan R. To Embed or Not: Network Embedding as a Paradigm in Computational Biology. Front Genet 2019; 10:381. [PMID: 31118945 PMCID: PMC6504708 DOI: 10.3389/fgene.2019.00381] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Accepted: 04/09/2019] [Indexed: 12/20/2022] Open
Abstract
Current technology is producing high throughput biomedical data at an ever-growing rate. A common approach to interpreting such data is through network-based analyses. Since biological networks are notoriously complex and hard to decipher, a growing body of work applies graph embedding techniques to simplify, visualize, and facilitate the analysis of the resulting networks. In this review, we survey traditional and new approaches for graph embedding and compare their application to fundamental problems in network biology with using the networks directly. We consider a broad variety of applications including protein network alignment, community detection, and protein function prediction. We find that in all of these domains both types of approaches are of value and their performance depends on the evaluation measures being used and the goal of the project. In particular, network embedding methods outshine direct methods according to some of those measures and are, thus, an essential tool in bioinformatics research.
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Affiliation(s)
- Walter Nelson
- Genetics and Genome Biology, SickKids Research Institute, Toronto, ON, Canada
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada
| | - Marinka Zitnik
- Department of Computer Science, Stanford University, Stanford, CA, United States
| | - Bo Wang
- Department of Computer Science, Stanford University, Stanford, CA, United States
- Peter Munk Cardiac Center, University Health Network, Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA, United States
- Chan Zuckerberg Biohub, San Francisco, CA, United States
| | - Anna Goldenberg
- Genetics and Genome Biology, SickKids Research Institute, Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Roded Sharan
- School of Computer Science, Tel Aviv University, Tel Aviv, Israel
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